A geochemical speciation model was developed to predict Distribution coefficients (K_ds) of radionuclides (RNs) in rivers. The model takes into account complexation of RNs with inorganic ligands, sorption of RNs with hydrous ferric oxides, complexation of RNs with dissolved and particulate organic carbon (DOC and POC) and sorption and/or co-precipitation of RNs to carbonates. A sorption model of Cs onto clay was also integrated. The tool is also designed to conduct uncertainty and sensitivity analysis. Sensitivity analysis follows a stepwise structured approach, starting from computationally ‘inexpensive’ Morris method to most costly variance-based EFAST method. A nested Monte Carlo approach was also implemented to separate natural variability and lack of knowledge in global uncertainty assessment. As case studies, K_d distributions were estimated for Co, Mn, Ag and Cs in seven French rivers. Uncertainty analysis allowed to quantify K_d ranges that can be expected when considering all the sensitive parameters together.

建立了地球化学形态模型以预测分布系数（K _ds）河流中的放射性核素（RNs）。该模型考虑了RNs与无机配体的络合，RNs与含水三氧化二铁的吸附，RNs与溶解的和颗粒状有机碳（DOC和POC）的络合以及RNs吸附和/或共沉淀为碳酸盐。还整合了Cs在粘土上的吸附模型。该工具还旨在进行不确定性和敏感性分析。灵敏度分析遵循逐步结构化的方法，从计算上“便宜的”莫里斯方法开始，到最昂贵的基于方差的EFAS​​T方法。还实施了嵌套蒙特卡洛方法，以将自然变异性和全局不确定性评估中缺乏知识分开。作为案例研究，K _d估算了法国七条河流中Co，Mn，Ag和Cs的分布。不确定性分析允许量化同时考虑所有敏感参数时可以预期的K _d范围。